Clustering patients according to health perceptions

Journal of Psychosomatic Research 56 (2004) 323 – 332

Clustering patients according to health perceptions Relationships to psychosocial characteristics and medication nonadherence Maida J. Sewitch a,*, Karen Leffondre´ b,c, Patricia L. Dobkin b,d a

Groupe de recherche interdisciplinaire en sante´ (GRIS), Faculte´ de me´decine, Universite´ de Montre´al C.P. 6128, succ. Centre-ville, Montreal, Que´bec, Canada, H3C 3J7 b Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Que´bec, Canada c Department of Epidemiology and Biostatistics, McGill University, Montreal, Que´bec, Canada d Department of Medicine, McGill University, Montreal, Que´bec, Canada Received 24 December 2002; accepted 3 July 2003

Abstract Background: Little is known about how patients rate their health perceptions. Our objectives were to identify systematic multivariate patterns of perceptions using cluster analysis, and to investigate associations among the clusters, psychosocial characteristics and medication nonadherence. Methods: Demographic, clinical and psychosocial data on 200 patients with inflammatory bowel disease (IBD) were collected prior to the index office visit and health perceptions were collected afterwards. Cluster analysis using a k-means method was used to identify subgroups of patients based on their responses to the Patient – Physician Discordance Scales (PPDS), an instrument that assesses perceptions of health status and of the clinical visit. Results: We identified five different patient groups: a ‘‘healthy, not distressed, good communication, low expectation for medication/testing’’ group; a ‘‘healthy, relatively distressed, good communication, high

expectation for medication, low expectation for testing’’ group; a ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group; a ‘‘healthy, not distressed, good communication, high expectation for medication/testing’’ group; and a ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group. After adjustment for age, sex, language, form of IBD, and disease activity, statistically significant between-clusters differences were found in psychological distress, social support satisfaction and medication nonadherence. Conclusions: Distinct patterns of patients’ health perceptions correlated with psychological health and adherence to treatment. This categorization may be used to help identify patients at higher risks for ineffective communication and nonadherence to medication. D 2004 Elsevier Inc. All rights reserved.

Keywords: Cluster analysis; Health perceptions; Inflammatory bowel disease; Patient adherence; Psychosocial factors

Introduction Physicians are often unaware of their patients’ health perceptions [1– 3]. Nonetheless, health perceptions contribute to the total impact of disease upon the individual. Patients’ perceptions may not correspond with objective physical health, since they reflect feelings, attitudes and beliefs about health status, yet they influence participation in physical, occupational, social and familial activities [4,5]. Perceptions of poorer health are related to negative

* Corresponding author. Tel.: +1-514-343-6111; fax: +1-514-343-2207. E-mail address: [email protected] (M.J. Sewitch). 0022-3999/04/$ – see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/S0022-3999(03)00508-7

emotional states [5,6], decreased patient adherence [7], and increased use of health services [4,8]. Studies show that when physicians’ understand their patients’ health perceptions, patients are likely to experience favourable health outcomes [9 –11], including decreased pain [12,13], improved physical and mental functioning [12], and adherence to appointment keeping [14,15]. Physicians’ lack of awareness of patients’ health perceptions may be most detrimental in chronic diseases that require long-term responsibility for implementing medical directives. Inflammatory bowel disease (IBD) is a prototypical chronic disease characterized by exacerbations and remissions [16]. In IBD, patients’ perceptions of poorer health are not correlated with findings derived by physical

324

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

examination or laboratory testing [17] or with physicians’ ratings of disease activity [18]. However, in IBD as in other medical conditions, the evaluation of patients’ perceptions often is limited to a single aspect of health. Given the inherently multidimensional nature of patients’ perceptions, we believe that more insight can be gained through simultaneous consideration of perceptions related to different aspects of health status, treatment and the medical visit. We, therefore, developed a classification system based on multivariate patterns of patients’ perceptions related to 10 aspects of the disease and the medical encounter. The goals of this study were to determine whether meaningful clusters of patients with similar health perceptions could be identified, and, if so, to determine whether these clusters were related to psychological distress, perceived stress, social support satisfaction and nonadherence to medication. Such profiles may ultimately help promote a uniform description of patients across chronic diseases and clinical settings.

Methods Recruitment and data collection This prospective study of patients’ and physicians’ perceptions in IBD was conducted at three university hospital gastroenterology clinics in Montreal, Canada, between February and November, 1999. Further details on study methodology are presented elsewhere [19]. In brief, 11 physicians affiliated with the McGill University Health Centre were mailed an introductory packet of information that invited them to participate. Patients were eligible for study inclusion if they were aged 18 years or older, had a confirmed diagnosis of Crohn’s disease or ulcerative colitis for at least 6 months, were fluent in English or French, and were not pregnant at recruitment. Consecutive IBD patients of participating physicians with scheduled appointments were approached while in the waiting room prior to a scheduled office visit. Written informed consent was obtained. Demographic, clinical and psychosocial data were collected prior to the office visit. Information on patients’ perceptions and medical recommendations were gathered immediately after the visit. Nonadherence to medication was assessed two weeks later by telephone interview and mail-back. Data on medications were obtained by chart review. The research protocol was approved by the McGill University Faculty of Medicine Institutional Review Board. Measurement instruments The following self-report, psychometrically sound instruments were employed. The patient version of the Patient – Physician Discordance Scale (PPDS), a 10-item questionnaire that employs a 100-mm visual analog scale

(VAS) response format (Table 1), was used to assess patients’ perceptions of health and of the clinical visit [20]. Scores range from 0 to 100 with higher scores indicating more of the attribute. Psychological distress was assessed with the Global Severity Index subscale of the Symptom Checklist-90-R, a 90-item measure that assesses various symptoms of distress that occurred during the past week [21]. Descriptive results are reported as t scores (normative mean = 50, S.D. = 10); t scores of 63 and greater are considered clinically important. Perceived stress was assessed with the Perceived Stress Scale, a 10-item measure of the extent to which an individual feels overwhelmed by stressful situations that occurred during the past month [22]. Scores range from 0 to 40, with higher scores indicating greater perceived stress [23]. The Social Support Questionnaire-6, a six-item measure, was used to assess satisfaction with social support [24,25]. Scores range from 1 to 6, with higher scores indicating greater satisfaction. Disease activity was assessed by physicians using a horizontal 100-mm VAS question, ‘‘how active the patient’s disease has been in the past 24 h’’, higher scores indicating higher disease activity. Physician and patient ratings of disease activity were moderately and statistically significantly correlated (r = .49, P = .0001). Medication nonadherence was assessed with a validated four-item questionnaire [26]. Patients were asked if they had (1) forgotten or (2) been careless in taking medicine, and if they (3) stopped medicine when feeling better or (4) worse. Three indices were created. Overall nonadherence was equal to 1 if patients answered yes to a least one of the four items above, and 0 otherwise. Intentional nonadherence, defined as stopping medication in response to feeling better or worse was equal to 1 if Item 3 or 4 equals 1, and 0 otherwise. Unintentional nonadherence, defined as being forgetful or careless, was equal to 1 if Item 1 or 2 equals 1, and 0 otherwise. In our IBD patients, internal reliability was moderate, Cronbach’s a = .54 for unintentional, .52 for intentional nonadherence, and .50 for overall

Table 1 Questions on the patient version of the Patient – Physician Discordance Scale The following questions refer to your health and functioning in the past 24 hours 1. How do you rate your abdominal pain? 2. How active has your disease been? 3. How do you rate your physical functioning? 4. How do you rate your psychological distress? 5. How do you rate your emotional well-being? The following questions refer to today’s visit with the doctor 6. To what extent was your main concern/problem discussed? 7. To what extent did you and your doctor discuss personal issues that might affect your disease? 8. To what extent did you expect to receive a prescription? 9. To what extent did you expect to be sent for further testing? 10. To what extent were you satisfied with this visit? All responses are recorded along 100-mm visual analog scales (0 – 100).

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

325

nonadherence. Similar to other studies [26 – 28], nonadherence to any medication was assessed because most patients with IBD are prescribed multiple medications [29].

Results

Statistical analyses

Ten (90.9%) invited physicians and 200 of 207 (96.6%) of their patients agreed to participate and provided written informed consent. Of these patients, 198 (99%) with complete baseline data comprise the sample on which the cluster analysis was performed; 153 (76.5%) with complete data at all time points comprise the sample on which the analysis for medication nonadherence was performed. One hundred eighty (90.0%) patients mailed-back the questionnaire on medication nonadherence, of whom 158 (87.8%) indicated they took medication. After accounting for medication information in patients’ medical records, we found that 16 (8.0%) patients had inconsistent data: 10 with medication but no adherence data and 6 with adherence but no drug data. These 16 patients were not statistically significantly different from those included in the analysis on demographic, clinical or psychosocial variables (data not shown).

Cluster analysis was used to classify patients into homogeneous subgroups according to their health and clinical visit perceptions, based on the 10 PPDS items (Table 1). This was performed with a k-means method [30] using the SAS procedure FASTCLUS [31], which assigns each patient to one and only one cluster. Three criteria are suggested for choosing number of clusters: (1) the observed overall R2 (ratio between-clusters variance/sum of between- and within-clusters variance); (2) the pseudo-F statistics (ratio between-clusters mean square/within-clusters mean square [32]; (3) the cubic clustering criterion (CCC; which compares the observed overall R2 to the approximate expected R2 under the null hypothesis that the data are sampled from a uniform distribution, assuming that the variables are uncorrelated) [33]. For each criterion, a local peak and/or a substantial increase associated with increasing number of clusters may indicate the optimal number of clusters [34]. However, since no criterion is completely satisfactory to choose the number of clusters and their results often differ [35,36], the final choice was based on a trade-off between the statistical criteria and the interpretability and clinical relevance of results. The number of clusters was a priori restricted to the 3 to 6 range. Once identified, the clusters were characterized in terms of mean values of individual item scores. Differences between clusters were assessed using Kruskal – Wallis test for continuous characteristics and chisquare or Fisher’s Exact Test for categorical variables. Multiple linear regression was carried out using the SAS procedure REG [31], to examine the relations between psychological variables and clusters, controlling for age, sex, language (English/French), disease activity, and form of IBD (ulcerative colitis/Crohn’s disease). Psychological distress, perceived stress, and social support satisfaction were used as continuous dependent variables in separate models, which included dummy variables representing the clusters. To test whether the k cluster indicator variables improved significantly the model’s fit to the data, we carried out the corresponding multiple-partial F test on k-1 numerator degrees of freedom (df). Tukey tests were used to perform pairwise comparisons of the means of the different clusters, while accounting for the impact of multiple comparisons on type I error, using the SAS procedure GLM [31]. Multiple logistic regression was performed to explore the associations between nonadherence variables and clusters, controlling for the potential confounders above, using the SAS procedure LOGISTIC [31]. Statistical significance of adjusted differences between the k clusters was assessed using the (k 1) df likelihood ratio test.

Subjects

Cluster analysis Table 2 shows the pseudo-F statistics, CCC and observed overall R2 for the analyses with three to six clusters. While the CCC suggests five clusters, both the pseudo-F and the observed overall R2 suggest four or five clusters. We, therefore, examined these two cluster solutions, and chose the five clusters since it provided the most interesting results and allowed us to better discriminate patients on their prescription and further testing expectations. These five clusters accounted for approximately 42% of total variance of the 10 PPDS items scores (Table 2). As shown in Table 3, the five clusters accounted for approximately 66% of the total variance of prescription expectation, and only 18% of the total variance of personal issues discussion. Thus, the dissimilarity between the five clusters is highest for expectation concerns for prescription

Table 2 Statistical criteria for the number of clusters Number of clusters Criterion

3

4

5

6

Pseudo-F statisticsa Cubic clustering criterionb Observed overall R2c

35.74 2.02 0.27

35.86 2.86 0.36

34.71 5.46 0.42

32.88 7.26 0.46

a The pseudo-F statistics is the ratio of between-clusters mean square to within-clusters mean square. b The CCC compares the observed overall R2 to the approximate expected R2 under the null hypothesis that the data are sampled from a uniform distribution, assuming that the variables are uncorrelated. c The observed overall R2 is the percentage of the total variance in the scores on items used to define the clusters that is explained by the clusters structure, i.e., the ratio of the between-clusters variance to the sum of between- and within-clusters variance.

326

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

and further testing, and lowest for emotional well-being and personal issues discussion. Fig. 1 compares the mean scores of individual items for the five clusters. Cluster 1, termed the ‘‘healthy, not distressed, good communication, low expectation for medication/testing’’ group, consists of 46 patients with minimal health-related complaints and low expectations for medications and further testing. These patients report very low psychological distress and very high emotional well-being, and high scores on discussions of the main problem and personal issues, and patient satisfaction. Cluster 2, termed the ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ group, is similar to Cluster 1 except that the 50 patients in this cluster had high expectations regarding medication. Cluster 3, termed the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group, consists of 34 patients with the most symptoms and highest level of distress. These patients are very symptomatic, report poor psychological health, communicate their concerns to their physicians, expect medication and testing, and are relatively satisfied with the visit. Cluster 4 (n = 38), termed the ‘‘healthy, not distressed, good communication, high expectation for medication/testing’’ group, is similar to Clusters 1 and 2 except for expectations. Despite having mild symptoms, Cluster 4 has high expectations for both medication and testing. Patients in Cluster 5 (n = 30), termed the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group, report moderate physical and mental health symptoms, do not expect medication or further testing, do not communicate concerns and are less satisfied with the visit, compared to patients in other clusters. Characteristics of clusters Baseline patient characteristics by the five clusters are presented in Table 4. As expected, the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group (Cluster 3) had, on average, higher Table 3 Percentage of the total variance explained by the five clusters for each variable included in the cluster analysis Variablea

Observed R2

Abdominal pain Disease activity Physical limitation Psychological distress Emotional well-being Discussed main problem Discussed personal issues Expected medication Expected further testing Satisfaction with visit

.36 .45 .36 .29 .20 .43 .18 .66 .59 .34

a

Variables corresponding to the 10 items of the patients version of the Patient – Physician Discordance Scale (see Table 1).

Fig. 1. Graphs comparing the mean scores of the 10 Patient – Physician Discordance Scale items for the five clusters.

Table 4 Characteristics of the five clusters based on patient health perceptions

Characteristics

Range

a b c

Cluster 3 Symptomatic, distressed, good communication high expectation for medication/ testing (n = 34)

Cluster 4 Healthy, not distressed, good communication high expectation for medication/testing (n = 38)

Cluster 5 Relatively healthy, relatively distressed poor communication low expectation for medication/testing (n = 30) p Valuea

36.6 34.8 60.9 23.9

37.8 38.0 74.3 16.0

36.4 47.1 70.6 17.7

35.5 50.0 79.0 5.3

36.9 36.7 43.3 30.0

34.8 32.6 32.6

22.0 42.0 36.0

29.4 29.4 41.2

47.4 18.4 34.2

33.3 20.0 46.7

78.3 29.1 11.0 15.5 4.9

62.0 29.8 11.4 14.7 5.3

76.5 57.8 10.5 19.2 5.0

55.3 29.4 10.2 19.3 5.9

73.3 33.8 11.9 13.9 5.1

.1143 .0003 .9440 .0801 .8231

37.0 8.7 47.8 6.5

46.0 16.0 32.0 6.0

17.7 35.3 35.3 11.8

44.7 7.9 36.8 10.5

36.7 3.3 56.7 3.3

.0095

53.7 14.2 5.5 3.3

57.8 18.6 5.6 3.6

65.1 21.3 5.2 3.3

57.4 14.8 5.5 3.6

61.3 19.0 4.8 3.0

.0001 .0001 .0001 .3784

73.9 50.0 19.6 63.0

78.0 30.0 8.0 86.0

94.1 81.8 36.4 97.1

63.2 81.6 10.5 92.1

86.7 50.0 23.3 76.7

.0182 .0001 .0120 .0004

30.0 22.5 22.5

65.0 22.5 17.5

50.0 59.3 37.5

55.2 31.0 27.6

59.3 29.6 14.8

.0250 .0062 .2265

11.5 38.5 42.3

18.2 22.7 36.4

6.3 18.8 21.9

20.0 56.7 66.7

15.4 38.5 42.3

.5315 .0103 .0094

Kruskall – Wallis for continuous and chi-square or Fisher’s Exact Tests for categorical variables. Mismatch refers to French-speaking patient/English-speaking physician. In minutes. Global Symptom Index t scores of the Symptom Checklist 90-Revised.

.9888 .5673 .0168 .0824 .2086

327

d

Cluster 2 Healthy, relatively distressed, good communication high expectation for medication, low for testing (n = 50)

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

Demographic Age (mean) 18.0 – 83.7 Male (%) English (%) Mismatched on languageb(%) Education (%) Primary/Secondary Junior college University and more Clinical Crohn’s Disease (%) Disease activity (n = 193) 0 – 99 Duration of disease (years) .7 – 51.4 Duration of office visitc (min) 7 – 45 Years with current physician .002 – 31 Reason for office visit (%) Routine Symptom exacerbation Follow-up visit Other Psychosocial (mean) Psychological distressd 30 – 81 Perceived stress 0 – 36 Social support satisfaction 1.8 – 6 Social support network size 0.2 – 9 Physician recommendation for further interventions (%) Schedule appointment Further investigation Consult with another health professional Medication Prescribed medication (%) (n = 168) 5-aminosalicylic acid Steroid Immunosuppressant Nonadherence to medication (%) (n = 158) Intentional Unintentional Overall nonadherence

Cluster 1 Healthy, not distressed, good communication low expectation medication/ testing (n = 46)

328

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

disease activity, perceived stress, and psychological distress. The ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group (Cluster 5) had the lowest proportion of English-speaking patients, the highest proportions of patient – physician mismatch on language and of follow-up visits, the shortest duration of the clinical visit, the lowest ratings on discussions on main problem and personal issues, and was least satisfied with both the office visit and with their social support. As expected, Cluster 3, with very symptomatic patients, was significantly more likely to receive physician recommendations for further interventions. However, this group’s expectation for medication was lower compared to Clusters 2 and 4, even though patients in the latter two clusters were quite healthy. Nonetheless, Clusters 2 and 4 received numerous prescriptions for medication. Drugs for mild to moderate disease, such as 5-aminosalicylic acid [37,38], were prescribed most frequently to the ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ group and least frequently to the ‘‘healthy, not distressed, good communication, low expectation for medication/testing’’ group ( P = .0250). Steroids, which are used for mild to severe disease [37,38], were prescribed to a greater proportion of

patients in Cluster 3 and the smallest proportion of patients in Clusters 1 and 2 ( P = .0062). Immunosuppressant drugs [37,38], which are used in severe disease, were prescribed twice as often to patients in Cluster 3 than to those in Cluster 5, although the difference did not reach statistical significance. Sixty-five (41.1%) patients reported being nonadherent in the 2 weeks since the clinical visit. The ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group (Cluster 3) was most likely to adhere to medication (overall and unintentional nonadherence), while the ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ group (Cluster 2) was most likely not to adhere. Psychosocial variables Table 5 shows the association between each of the psychological variables and the five clusters, adjusted for potential confounders. Note that to facilitate the interpretation of resulting regression coefficients, we chose Cluster 3, the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group, as the reference category as it was the group with the highest distress and

Table 5 Associations between psychosocial variables and the five clusters Dependent variablea Psychological distressb Cluster group name 1 Healthy, not distressed, good communication, low expectations (n = 46) 2 Healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing (n = 50) 3 Symptomatic, distressed, good communication, high expectation for medication/testing (n = 34) 4 Healthy, not distressed, good communication, high expectation for medication/testing. (n = 38) 5 Relatively healthy, relatively distressed, poor communication, low expectation for medication/testing (n = 30) 4 df, F testf a

be (95% CI)

Perceived stressc P value

Social support satisfactiond

b (95% CI)

0.58 ( .77,

.40)

.0001

6.76 ( 9.96,

0.48 ( .67,

.30)

.0001

2.63 ( 0.55, 5.81)

0 (ref )

3.56)

P value

b (95% CI)

P value

.0001

0.30 ( .003, .60)

.0539

.1064

0.38 (.08, .68)

.0127

0 (ref )

0 (ref )

0.53 ( .73,

.33)

.0001

6.17 (09.55,

2.80)

0.22 ( .42, 7.32

.01)

.0371 .0001

2.04 ( 5.49, 1.42) 3.45

.0004

0.31 ( .006, .63)

.0563

.2495 .0006

0.43 ( .75, 4.21

.0104 .0001

.10)

In each model, the dependent variable is the psychosocial variable and the independent variables are the indicators of each cluster, except Cluster 3 which is the reference, and the potential confounders: age, sex, language, form of IBD, physician’s rating of disease activity. b Higher values indicate more psychological distress (raw scores range from 0.02 to 2.31). c Higher values indicate more perceived stress (scores range from 0 to 40). d Higher values indicate more satisfaction with social support (scores range from 1 to 6). e b represents the adjusted difference in the mean of the corresponding psychosocial variable (dependent variable) between a given cluster and Cluster 3 (reference group); CI, confidence interval. f Multiple-partial F test on 4 numerator df to test whether the 4 clusters indicator variable improve significantly the model’s fit to data.

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

329

Table 6 Associations between medication nonadherence and the five clusters Dependent variablea Overall nonadherence Cluster group name 1 Healthy, not distressed, good communication, low expectation for medication/testing (n = 46) 2 Healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing (n = 50) 3 Symptomatic, distressed, good communication, high expectation for medication/testing (n = 34) 4 Healthy, not distressed, good communication, high expectation for medication/testing. (n = 38) 5 Relatively healthy, relatively distressed, poor communication, low expectation for medication/testing (n = 30)

Unintentional nonadherence

Intentional nonadherence

P value

OR (95% CI)

P value

OR (95% CI)

P value

3.99 (1.03, 15.43)

.0447

3.21 (.78, 13.27)

.1078

2.15 (.28, 16.74)

.4663

3.26 (.94, 11.29)

.0621

1.37 (.36, 5.25)

.6510

4.56 (.74, 27.98)

.1014

b

OR (95% CI)

1.00 (ref)

1.00 (ref)

1.00 (ref)

10.73 (2.73, 42.16)

.0007

6.79 (1.66, 27.72)

.0076

4.79 (.72, 31.88)

.1056

4.25 (1.15, 15.63)

.0297

4.41 (1.11, 17.50)

.0349

2.55 (.38, 17.31)

.3372

a

In each model, the dependent variable is the nonadherence variable (overall, unintentional, and intentional) and the independent variables are the indicators of each cluster, except Cluster 3 which is the reference, and the potential confounders: age, sex, language, form of IBD), physician’s rating of disease activity. b OR, odds ratio representing the risk of nonadherence for a given cluster compared to Cluster 3 (reference group); CI, confidence interval.

perceived stress (Table 4). For each model, the multiplepartial F statistics for the group of four dummy cluster variables was highly significant, indicating that the addition of these variables improved significantly the models that included only the potential confounders. This demonstrates that significant differences between the clusters exist that are independent of variables controlled in our analyses. Table 5 shows that, even after adjustment for potential confounders, Clusters 1, 2, 4 and 5 had significantly lower psychological distress than Cluster 3. Tukey tests revealed that Clusters 1 (the ‘‘healthy, not distressed, good communication, low expectation for medication/testing’’ group) and 4 (the ‘‘healthy, not distressed, good communication, high expectation for medication/testing group’’) had significantly lower psychological distress than Cluster 5 (the ‘‘relative healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group) (minimum significant difference = 0.25). Clusters 1 and 4 had significantly lower perceived stress than Cluster 3, and Tukey test indicated that Cluster 2, 3, and 5 all had significantly lower perceived stress than Cluster 1 (minimum significant difference = 4.33). Clusters 1, 2, 3 and 4 had significantly higher social support satisfaction than Cluster 5 (minimum significant difference = 0.41), indicating that the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group was least satisfied with social support.

Nonadherence to medication Table 6 presents the summary of the results of the multivariate logistic modeling of medication nonadherence. To facilitate the interpretation of resulting odds ratios, we chose Cluster 3, the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group, as the reference category, as it was the group with the lowest risk of nonadherence (Table 4). The results indicate that, after adjustment for potential confounders, Clusters 1, 4 and 5 were associated with significantly higher risks for overall nonadherence than Cluster 3. Furthermore, Cluster 2, the ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ group, was associated with a marginally higher risk of nonadherence to medication. For unintentional nonadherence, Cluster 4, the ‘‘healthy, not distressed, good communication, high expectation for medication/testing’’ group, had a significantly higher risk, as did Cluster 5, the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group, compared to the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ patients. Compared to Cluster 3, Clusters 1 and 2 had no significantly higher risk of unintentional nonadherence. For intentional nonadherence, cluster groupings remained not statistically significant, indicating the absence of systematic differences between clusters.

330

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

Discussion In the present study, we employed cluster analysis to classify IBD patients on multivariate profiles of their health and clinical visit perceptions. Five clusters were identified which differed most by patients’ expectations for medication and further testing, and to a lesser extent, by their perception of disease activity and of the extent to which they discussed their main problem with their physician. The clusters were associated with particular demographic, clinical, and psychosocial variables, recommended interventions, prescribed medication, and medication nonadherence. As indicated from the perception profiles, the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group which, as expected, had more severe disease, greater psychological distress and perceived stress, was more likely to receive recommendations for interventions and to adhere to medication. The ‘‘healthy, not distressed, good communication, high expectation for medication/testing’’ group had the lowest proportion of patients with Crohn’s disease and were most likely to not take their medication. The ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group had the least satisfaction with social support, was second most likely not to take prescribed, and had the largest percentage of patient –physician mismatch on language, which may impede effective communication. Similar levels of abdominal pain, disease activity and physical functioning were found in the ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ and the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ groups, but patients in the latter had poorer psychosocial functioning. Cluster groupings were related to psychosocial functioning. Compared to other groups, the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group had as expected, on average, higher psychological distress and perceived stress as well as the most severe physical and psychological symptoms. Thus, the more symptoms patients had, the poorer their mental health is, a finding that mirrors the literature on disease severity and psychological functioning [39 – 41]. The ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group had, on average, the lowest satisfaction with social support, and rated discussions with their physicians lowest as well, which suggests this group fits the profile of patients described as ‘‘difficult to treat,’’ who are, typically, more distressed [42], socially isolated [43], and dissatisfied [42 – 44]. Whereas patient –physician language mismatch may account for poor communication and short visit duration, the fact that this cluster also report low social support implies the difficulty in forming social relationships may rest with the patient rather than with the patient –physician interaction. Not only does the inability

to connect with physicians place these individuals at risk of poorer health outcomes [45], but they are also unlikely to benefit from the health-promoting effects of social support [46 – 48], which, in turn, increases their risks of depression [49] and increased use of health services [50,51]. Therefore, patients in this cluster may be the most challenging for physicians to work with effectively. Cluster groupings were also related to overall nonadherence to medication, and appears to be due to unintentional rather than intentional behaviours. Because the ‘‘symptomatic, distressed, good communication, high expectation for medication/testing’’ group was at lowest risk of overall and unintentional nonadherence, perceptions of active disease and poorer mental health may motivate patients to adhere to prescribed medication. Even after adjusting for sociodemographic and clinical characteristics, Cluster 4 was 10 times (95% CI = 2.7 –42.2) more likely to not adhere to medication, and nearly seven times (95% CI = 1.7 –7.7) more likely to be unintentionally nonadherent than Cluster 3. This may, in part, be a result of the different medications prescribed to the two groups. Whereas the ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ group received more 5-aminosalicylicacids, which may be more effective at maintaining remission in ulcerative colitis than Crohn’s disease [47], the ‘‘symptomatic, distressed, good communication, high expectations’’ group received more steroids, which are usually effective at inducing remission in both ulcerative colitis and Crohn’s disease [31]. Because of the increased risk of unintentional nonadherence associated with the ‘‘healthy, not distressed, good communication, high expectation for medication/testing’’ group, further exploration sought to determine if this group was less likely to believe in the beneficial effects of medication. Significant between-clusters differences were found on the mean ratings on the VAS item ‘‘How certain are you that the medication will positively affect your health?’’ ( P = .0093). The ‘‘healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing’’ group was most likely (mean = 78.9) and the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group least likely (mean = 54.4) to believe in the benefit of medication (data not shown). While these findings do not explain the increased risks of nonadherence in the ‘‘healthy, not distressed, good communication, high expectation for medication/testing’’ group, they elucidate why the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group was more than four times more likely to be nonadherent to medication compared to the ‘‘symptomatic, distress, good, good communication, high expectation for medication/testing’’ group. Rates of medication nonadherence in the present study are consistent with those reported in the IBD literature, which are about 20% for short-term and about 50% and

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

greater for long-term therapy [52 –55] and similar to other chronic diseases [56]. Although differences in the methods of assessment and in drug regimens (mono- vs. polytherapies) do not permit direct comparison to other studies, our findings corroborate others [28] in that intentional nonadherence was less common than unintentional. Collectively, our results suggest that poorer communication in the ‘‘relatively healthy, relatively distressed, poor communication, low expectation for medication/testing’’ group may limit the negotiation of realistic treatment and contribute to the patients’ lack of understanding regarding the need for and effectiveness of prescribed medication. Clusters were not related to intentional nonadherence, possibly because of the low frequency of self-reported intentional nonadherence. The clinical implication of our results is that it is possible for physicians to identify patients at risk of nonadherence by their constellation of characteristics and intervene. As indicated by our analyses, physicians may request that patients schedule follow-up appointments when medication and/or further investigation are recommended. Physicians may also reach out to patients when there are communication problems and spend more time explaining the importance of taking medication as prescribed, discuss nonadherence, and/or directly involve a bilingual clinic nurse in patient care. The take-home message is that physicians have to become proactive regarding effective communication when patients have poor social support and poor social skills. At least three study limitations have to be recognized. First, there is an inherent limitation of cluster analysis in that, in the absence of a single well-grounded criterion, the choice of the number of clusters has to rely on somewhat arbitrary decisions. Second, the enrollment of only patients with scheduled appointments means that our results may not be generalizable to all IBD clinic patients such as those presenting for procedures or emergency visits. Third, our study methodology did not permit testing the stability of the clusters over time because the characteristics used to identify the clusters were obtained at only one point in time. While we expect that IBD patients’ perceptions will differ depending on whether they are in active or inactive disease states [57] and that patients will not remain in the same clusters over time, whether the cluster characteristics remain the same is an area for future research. In summary, relatively homogeneous clusters of patients were identified based on their responses to 10 questions regarding their health perceptions. This categorization may help physicians identify patients at higher risks of ineffective communication, nonadherence to medication, and unfavourable health outcomes. Application of similar methods in other chronic diseases could indicate to what extent the classification of patients’ profiles found in the current study is generic rather than disease specific. Future research is needed to replicate these finding in IBD and to evaluate the relationships between perception profiles, and quality of life and treatment outcomes.

331

Acknowledgments This research was supported, in part, by grants from McGill University (Social Sciences and Humanities Committee) and the Montreal General Hospital Research Institute. Maida Sewitch is supported as a postdoctoral fellow by the Canadian Institutes of Health Research (CIHR). During this study, she was supported by Health Canada through a National Health Research and Development Program (NHRDP) PhD research training fellowship and by the Fonds de recherche en sante´ du Que´bec (FRSQ) through a PhD health professional training fellowship. Karen Leffondre´ is supported as a postdoctoral fellow by the National Cancer Institute of Canada (NCIC) under the PREECAN program. Patricia Dobkin is supported as a Scientist by the FRSQ. The authors thank Dr. Michal Abrahamowicz for his many helpful suggestions and comments.

References [1] Freidin RB, Goldman L, Cecil RR. Patient – physician concordance in problem identification in the primary care setting. Ann Intern Med 1980;93:490 – 3. [2] Boland BJ, Scheitel SM, Wollan PC, Silverstein MD. Patient – physician agreement on reason for ambulatory general medical examinations. Mayo Clin Proc 1998;73:109 – 17. [3] Lieberman JR, Dorey F, Shekelle P, Schumacher L, Thomas BJ, Kilgus DJ, Finerman GA. Differences between patients’ and physicians’ evaluations of outcome after total hip arthroplasty. J Bone Joint Surg 1996;78:835 – 8. [4] Connelly JE, Philbrick JT, Smith GR, Kaiser DL, Wymer A. Health perceptions of primary care patients and the influence on health care utilization. Med Care 1998;27:S99 – 109. [5] Boomsma MM, Bijl M, Stegeman CA, Kallenberg CGM, Hoffman GS, Cohen Tervaert JW. Patients’ perceptions of the effects of systemic lupus erythematosus on health, function, income, and interpersonal relationships: a comparison with Wegener’s granulomatosis. Arthritis Rheum 2002;47:196 – 201. [6] Olfson M, Gilbert T, Weissman M, Blacklow RS, Broadhead WE. Recognition of emotional distress in physically healthy primary care patients who perceive poor physical health. Gen Hosp Psychiatry 1995;17:173 – 80. [7] Sherbourne CD, Hays RD, Ordway L, DiMatteo MR, Kravitz RL. Antecedents of adherence to medical recommendations: results from the Medical Outcomes Study. J Behav Med 1992;15:447 – 8. [8] deBoer AGEM, Wijker W, deHaes HCJM. Predictors of health care utilization in the chronically ill: a review of the literature. Health Policy 1997;42:101 – 15. [9] Bass MJ, Buck C, Turner L, Dickie G, Pratt G, Robinson HC. The physician’s actions and the outcome of illness in family practice. J Family Pract 1986;23:43 – 7. [10] Stewart MA, McWhinney IR, Buck CW. The doctor/patient relationship and its effect upon outcome. J R Coll Gen Pract 1979;29:77 – 82. [11] Starfield B, Wray C, Hess K, Gross R, Birk PS, D’Lugoff BC. The influence of patient – practitioner agreement on outcome of care. Am J Pub Health 1981;71:127 – 31. [12] Berkanovic E, Hurwicz M-L, Lachenbruch PA. Concordant and discrepant views of patients’ physical functioning. Arthritis Care Res 1995;8:95 – 101.

332

M.J. Sewitch et al. / Journal of Psychosomatic Research 56 (2004) 323–332

[13] Wartman SA, Morlock LL, Malitz FE, Palm E. Impact of divergent evaluations by physicians and patients of patient complaints. Public Health Rep 1983;98:141 – 5. [14] Goldman L, Freidin R, Cook F, Eigner J, Grich P. A multivariate approach to the prediction of no-show behavior in a primary care center. Arch Intern Med 1982;142:563 – 7. [15] Chesney AP, Brown KA, Poe CW, Gary HE. Physician – patient agreement on symptoms as a predictor of retention in outpatient care. Hosp Commun Psychiatr 1983;34:737 – 9. [16] Feldman EJ. Medical treatment of inflammatory bowel disease: patient concerns. In: Kirsner JB, Shorter RG, editors. Inflammatory bowel disease—from bench to bedside. 4th ed. Baltimore (MD): Williams & Wilkins, 1994. pp. 445 – 50. [17] Hanauer SB. Measurements of disease activity. In: Targan SR, Shanahan F, editors. Inflammatory bowel disease — from bench to bedside. Baltimore (MD): Williams and Wilkins, 1994. pp. 429 – 44. [18] Drossman DA, Patrick DJ, Mitchell CM, Zagami EA, Appelbaum ML. Health-related quality of life in inflammatory bowel disease. Functional status and patient worries and concerns. Dig Dis Sci 1989;34:1379 – 86. [19] Sewitch MJ, Abrahamowicz M, Bitton A, Daly D, Wild GE, Cohen A, Katz S, Szego PL, Dobkin PL. Psychosocial correlates of patient – physician discordance in inflammatory bowel disease. Am. J Gastroenterol 2002;97:2174 – 83. [20] Sewitch MJ, Abrahamowicz M, Dobkin PL, Tamblyn R. Measuring differences between patients’ and physicians’ perceptions: the Patient – Physician Discordance Scale. J Behav Med 2003;26:245 – 64. [21] Derogatis LR. SCL-90-R. Symptom Checklist-90-R. Administration, scoring and procedures manual. 3rd ed. Minneapolis (MN): National Computer Systems Inc, 1994. [22] Cohen S, Williamson GM. Perceived stress in a probability sample of the United States. In: Spacapan S, Oskamp S, editors. The social psychology of health. Newbury Park (CA): Sage, 1988. pp. 31 – 67. [23] Cohen S, Williamson GM. Perceived stress in a probability sample of the United States. In: Spacapan S, Oskamp S, editors. The social psychology of health. Newbury Park (CA): Sage, 1988. pp. 31 – 67. [24] Sarason IG, Sarason BR, Shearin EN, Pierce GR. A brief measure of social support: practical and theoretical implications. J Soc Pers Relatsh 1984;4:497 – 510. [25] Sarason IG, Levine HM, Basham RB, Sarason BR. Assessing social support: the social support questionnaire. J Pers Soc Psychol 1983; 44:127 – 39. [26] Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986;24:67 – 74. [27] Kravitz RL, Hays RD, Sherbourne CD, DiMatteo MR, Rogers WH, Ordway L, Greenfield S. Recall of recommendations and adherence to advice among patients with chronic medical conditions. Arch Intern Med 1993;153:1869 – 78. [28] Choo PW, Rand CS, Inui TS, Lee M-LT, Cain E, Cordieiro-Breault, Canning C, Platt R. Validation of patient reports, automated pharmacy records. and pill counts with electronic monitoring of adherence to antihypertensive therapy. Med Care 1999;37:846 – 57. [29] Blomqvist P, Feltelius N, Lofberg R, Ekbom A. A 10-year survey of inflammatory bowel diseases-drug therapy, costs and adverse reactions. Aliment Pharmacol Ther 2001;15:475 – 81. [30] MacQueen JB. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967;1:281 – 97. [31] SAS Institute Inc. SAS/STAT User’s guide Version 8. 2000. Cary, NC USA: SAS Institute, Inc. [32] Calinski T, Harabasz J. A dendrite method for cluster analysis. Commun Stat 1974;3:1 – 27. [33] Sarle WS. Cubic clustering criterion. SAS Technical Report A-108. Cary (NC): SAS Institute Inc, 1983.

[34] Miligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika 1985;50: 159 – 79. [35] Everitt BS. Unresolved problems in cluster analysis. Biometrics 1979;35:169 – 81. [36] Hartigan JA. Statistical theory in clustering. J Classif 1985;2:63 – 76. [37] Botoman VA, Bonner GF, Botoman DA. Management of inflammatory bowel disease. Am Fam Phys 1998;57:57 – 68. [38] Hanauer, SB. Update on medical management of inflamatory bowel disease: ulcerative colitis. 2001;1:169 – 76. [39] Katon W, Sullivan MD. Depression and chronic medical illness. J Clin Psychiatry 1990;51:3 – 11. [40] Sternbach RA. Psychological dimensions and perceptual analyses, including pathologies of pain. In: Carterett ED, Friedman MD, editors. Handbook of perception. New York: Academic Press, 1978. pp. 231 – 61. [41] Dobkin PL, Fortin PR, Joseph L, Esdaile JM, Danoff DS, Clarke AE. Psychosocial contributors to mental and physical health in patients with systemic lupus erythematosus. Arthritis Care Res 1998; 11:23 – 31. [42] Mayou R, Sharpe M. Patients whom doctors find difficult to help. An important and neglected problem. Psychosomatics 1995;36:323 – 5. [43] Sharpe M, Mayou R, Seagroatt V, Surawy C, Warwick H, Bulstrode C, Dawber R, Lane D. Why do doctors find some patients difficult to help? Q J Med 1994;87:187 – 93. [44] Hahn SR, Kroenke K, Spitzer RL, Brody D, Williams JB, Linzer M, de Gruy FV. The difficult patient: prevalence, psychopathology and functional impairment. J Gen Intern Med 1996;11:1 – 8. [45] Kaplan SJ, Greenfield S, Ware JEj. Assessing the effects of physician – patient interactions on the outcomes of chronic disease. Med Care 1989;27:S110 – 27. [46] House JS, Landis KR, Umberson D. Social relationships and health. Science 1998;241:540 – 4. [47] Thoits PA. Conceptual, methodological and theoretical problems in studying social support as a buffer against life stress. J Health Soc Behav 1982;23:145 – 59. [48] Berkman L. The role of social relations in health promotion. Psychosom Med 1995;57:245 – 54. [49] Dalgard OS, Bjork S, Tambs K. Social support, negative life events and mental health. Br J Psychiatry 1995;166:29 – 34. [50] Pilisuk M, Boylan R, Acredolo C. Social support, life stress and subsequent medical care utilization. Health Psychol 1987;6:273 – 88. [51] Broadhead WE, Gehlbach SH, DeGruy FV, Kaplan BH. Functional versus structural social support and health care utilization in a family medicine outpatient practice. Med Care 1989;27: 221 – 32. [52] Riley SA, Mani V, Goodman MJ, Lucas S. Why do patients with ulcerative colitis relapse? Gut 1990;31:179 – 83. [53] Farup PG, Hovde O, Halvorsen FA, Raknerud N, Brodin U. Melsalazine suppositories versus hydrocortisone foam in patients with distal ulcerative colitis. A comparison of the efficacy and practicality of two topical treatment regimens. Scan J Gastroenterol 1995;30: 164 – 70. [54] van Hees PA, van Tongeren JH. Compliance to therapy in patients on a maintenance dose of sulfasalazine. J Clin Gastroenterol 1982; 4:333 – 6. [55] Kane SV, Cohen RD, Aikens JE, Hanauer SB. Prevalence of nonadherence with maintenance mesalamine in quiescent ulcerative colitis. Am J Gastroenterol 2001;96:2929 – 33. [56] DiMatteo MR. Adherence to treatment. In: Feldman MD, Christensen JF, editors. Behavioral medicine. A primary care handbook. 1st ed. New York (NY): Appleton & Lange, 1997. pp. 136 – 40. [57] Sewitch MJ, Abrahamowicz M, Bitton A, Daly D, Wild GE, Cohen A, Katz S, Szego PL, Dobkin PL. Psychological distress, social support and disease activity in patients with inflammatory bowel disease. Am J Gastroenterol 96:1470 – 9.